🧫 1. Epithelial Cells & Wound Healing Assay

Epithelial cells form surface layers in organs like the kidney. They are often studied to understand cell migration — how cells move to repair tissue damage.

• In lab experiments, cells are grown as a sheet, and then a scratch or wound is made using a scraper.
• Scientists then observe how quickly cells migrate to close the gap.
• This process helps study not only normal healing but also cancer metastasis, since cancer cells move and invade tissues in a similar way.

Fluorescent dyes like Hoechst stain are used to mark nuclei (bright dots in the image). By using software like Fiji/ImageJ, we can track how individual cells move, calculate their speed, and compare behavior at the wound’s edge versus deeper inside the layer.

Treatment with EGF (Epidermal Growth Factor) can reduce migration — showing how signaling molecules affect movement.

🔬 2. Confocal Microscopy

Confocal microscopy gives clearer, sharper images by eliminating blur from out-of-focus light.

How it works:

• Uses a pinhole to allow only light from a specific focal plane to reach the detector.
• Light from above or below the focal plane misses the pinhole and is blocked. → This produces a high signal-to-noise ratio and sharp optical sections.

Why it’s powerful:

• It can take multiple “optical slices” (Z-stacks) through thick samples.
• The computer then builds a 3D reconstruction or maximum projection image.

Limitation:

• Scanning laser confocal is slow because it scans point by point.
• Each scan exposes the sample to light, leading to photobleaching (loss of fluorescence) and photodamage, especially in live cells.

⚙️ 3. Spinning Disk Confocal Microscopy

A faster and gentler variant of confocal microscopy.

Key idea:

• Instead of one pinhole, there’s a disk full of pinholes that spins rapidly.
• This allows the microscope to collect light from many points simultaneously — thousands at once.

Advantages:

✅ Much faster imaging ✅ Less photodamage and bleaching ✅ Better for live-cell imaging

So:

• Scanning laser → better detail, slower, more damage.
• Spinning disk → slightly lower detail, much faster, gentler on cells.

🧩 4. Expansion Microscopy

When optical magnification hits physical limits, scientists came up with a creative trick: make the sample itself bigger!

How:

• Embed the specimen in a swelling gel (like the polymer used in diapers).
• Chemically cross-link the sample to the gel.
• Add water — the gel expands, stretching the sample with it.

This expansion increases physical distance between molecules, so normal microscopes can resolve structures previously too small to see.

Applications:

• Study membrane proteins, vesicle trafficking, and fine structures inside cells.
• Requires optimization but yields super-resolution results without special optics.

💻 5. Digital Images as Data

In science, microscope images are not just pictures, but numerical matrices — each pixel stores a number representing brightness (intensity).

• A black-and-white image is a matrix of intensity values.
  • 0 = black
  • higher values = brighter (white)
• In microscopy, there’s always some background noise, so “0” is rare.

❗ Important note:

Most microscope detectors don’t detect colors — they count photons. Colors in published figures (green, red, blue) are added later for clarity.

⚖️ 6. Bit Depth and Image Detail

“Bit depth” = how many intensity levels (gray shades) the image can store.

Higher bit depth = more detail and smoother gradations. If images are saved with too few bits or compressed, data is lost.

Some 16-bit microscope images may appear black in Windows Viewer — not because they’re broken, but because the viewer can’t display that range. Tools like Fiji handle this correctly.

📊 7. Dynamic Range & Histograms

Dynamic range shows how well an image uses the full intensity spectrum.

• Ideally, pixel values spread across the full range (e.g., 5–255 in 8-bit).
• If the range is too narrow → poor contrast.
• If pixels hit the upper limit (255) → saturation, data loss.

A histogram plots pixel counts (y-axis) versus intensity (x-axis):

• Left peak = background noise
• Right tail = real signal A broad distribution indicates good use of the camera’s range.

🎨 8. Color Channels and Crosstalk

When imaging multiple fluorophores (e.g., red and green), proper filter selection is crucial.

If filters overlap (detecting both colors), you get bleed-through or crosstalk — the camera can’t tell whether light came from the “red” or “green” molecule.

Control strategies:

• Use single-color controls (each fluorophore alone) to verify specificity.
• Ensure microscope filters separate emission bands correctly. Otherwise, you risk false interpretation of co-localization.

🧠 Summary of Key Concepts